Download Th17/Treg ratio derived using DNA methylation asthmatic response

Singh et al. Allergy, Asthma & Clinical Immunology 2014, 10:32
http://www.aacijournal.com/content/10/1/32
RESEARCH
ALLERGY, ASTHMA & CLINICAL
IMMUNOLOGY
Open Access
Th17/Treg ratio derived using DNA methylation
analysis is associated with the late phase
asthmatic response
Amrit Singh1,2,3†, Masatsugu Yamamoto1,2,4,5†, Jian Ruan1,2,3, Jung Young Choi1,2, Gail M Gauvreau6, Sven Olek7,
Ulrich Hoffmueller7, Christopher Carlsten1,2,4,5, J Mark FitzGerald2,4,5, Louis-Philippe Boulet8, Paul M O'Byrne6
and Scott J Tebbutt1,2,3,5*
Abstract
Background: The imbalance between Th17 and Treg cells has been studied in various diseases including allergic
asthma but their roles have not been fully understood in the development of the late phase asthmatic response.
Objectives: To determine changes in Th17 and Treg cell numbers between isolated early responders (ERs) and dual
responders (DRs) undergoing allergen inhalation challenge. To identify gene expression profiles associated with
Th17 and Treg cells.
Methods: 14 participants (8 ERs and 6 DRs) with mild allergic asthma underwent allergen inhalation challenge.
Peripheral blood was collected prior to and 2 hours post allergen challenge. DNA methylation analysis was used to
quantifiy the relative frequencies of Th17, Tregs, total B cells, and total T cells. Gene expression from whole blood
was measured using microarrays. Technical replication of selected genes was performed using nanoString nCounter
Elements.
Results: The Th17/Treg ratio significantly increased in DRs compared to ERs post allergen challenge compared to
pre-challenge. Genes significantly correlated to Th17 and Treg cell counts were inversely correlated with each other.
Genes significantly correlated with Th17/Treg ratio included the cluster of genes of the leukocyte receptor complex
located on chromosome 19q 13.4.
Conclusions: Th17/Treg imbalance post-challenge may contribute to the development of the late phase inflammatory
phenotype.
Keywords: Allergen inhalation challenge, Asthma, Asthmatic response, DNA methylation, Epigenetic cell counting,
Peripheral blood, Th17/Treg ratio, nCounter Elements
Introduction
The imbalance between a proinflammatory T helper 17
(Th17) and a regulatory T (Treg) cell phenotype may play
a crucial role in allergic airway inflammation [1]. Experimental models have shown that Th17 cells typically promote neutrophilic inflammation, and also play important
roles in airway hyperresponsiveness in concert with Th2
* Correspondence: [email protected]
†
Equal contributors
1
James Hogg Research Centre for Heart Lung Innovation, St. Paul’s Hospital,
University of British Columbia, Vancouver, BC, Canada
2
Institute for HEART + LUNG Health, Vancouver, BC, Canada
Full list of author information is available at the end of the article
cells [2]. In peripheral blood, Th17 cell counts have been
shown to be higher in subjects with allergic asthma compared to healthy controls [3,4]. The percentage of Th17
cells and IL-17 levels in peripheral blood have been shown
to be significantly elevated 24 hours after allergen challenge in dual responders compared to early responders or
healthy controls [5]. On the other hand, Treg cells maintain immune homeostasis and regulate immune responses
to allergens by preventing excessive inflammatory responses
[6]. Treg cells were originally identified as CD4+CD25+
T cells with a function to suppress immune responses
[7]. In order to identify Treg cells, FOXP3 expression
as a specific marker has been used, however, it is also
© 2014 Singh et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain
Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article,
unless otherwise stated.
Singh et al. Allergy, Asthma & Clinical Immunology 2014, 10:32
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expressed in activated non-suppressor T cells [8,9]. Low
levels of the IL-7 receptor (CD127) in combination with
high expression of CD4 and CD25 can be used to isolate
highly purified suppressive Tregs [10]. Recently, DNA
methylation analysis of the Treg specific demethylation
region (TSDR) within the FOXP3 locus has been used to
enumerate Treg cells, [11] which have been shown to
significantly correlate with CD4+CD25+CD127lo and
CD4+CD25+CD127loFOXP3+ cells [12].
We have previously demonstrated that peripheral blood
is a useful biological material with which to study changes
in the blood transcriptome, proteome and metabolome of
individuals with mild atopic asthma undergoing allergen
inhalation challenge [13-16]. In the present study, we have
used qPCR based DNA methylation analysis to estimate
the number of Th17 cells, Treg cells, T cells and B cells
in peripheral blood of mild atopic asthmatics undergoing allergen inhalation challenge. In the same individuals, we also analysed gene expression profiles in whole
blood using microarrays to identify genes correlated
with each cell type. We hypothesized that changes in
specific immune cell counts in peripheral blood would
be associated with the allergen-induced late phase asthmatic response.
Methods
Study participants and allergen inhalation challenge
The Institutional Review Boards of the participating institutions, University of British Columbia, McMaster University
and Université Laval, approved this study. Fourteen individuals were recruited as part of the AllerGen NCE Clinical
Investigator Collaborative (Canada) and provided written
informed consent to undergo an allergen inhalation challenge. All participants were non-smokers, free of other lung
diseases, and not pregnant. Diagnosis of asthma was based
on the Global Initiative for Asthma criteria. Participants
were diagnosed with mild allergic asthma, and only used
intermittent short-acting bronchodilators for treatment
of their asthma. Participants had a baseline FEV1 ≥ 70%
of predicted, and the PC20, provocative concentration of
methacholine required to produce a 20% decrease in
FEV1, was ≤16 mg/mL.
Skin prick tests were used to determine allergies to
cat, and the dose of cat allergen extract for inhalation.
Methacholine and allergen challenges were conducted as
triad visits. On the first and third day, participants underwent methacholine inhalation tests for assessments of airway hyperresponsiveness (AHR) as described previously
[17,18]. The allergen-induced shift (post/pre in PC20) was
evaluated as the change in AHR. On the second day participants underwent allergen inhalation challenge with extracts of cat pelt or hair in doubling doses until a drop in
FEV1 of at least of 20% was achieved, then FEV1 was measured at regular intervals up to 7 hours post-challenge as
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described previously [19]. All participants developed an
early response which resolved within 1–3 hours after challenge. Participants that demonstrated a maximum drop in
FEV1 of greater than 15% between 3 to 7 hours after allergen inhalation were classified as dual responders (DRs).
Participants having an FEV1 drop of 10% that was still falling at the end of the 7 hour observation period were categorized as DRs if they also demonstrated a drop in PC20
(post compared to pre methacholine challenge). Participants who showed neither a drop in FEV1 > 15% between
3 to 7 hours after challenge nor a decreased PC20 were
classified as isolated early responders (ERs).
Blood collection and isolation of RNA and DNA
Peripheral blood was obtained immediately before and
2 hours post-challenge in PAXgene Blood RNA tubes
(PreAnalytiX, Qiagen/BD, Valencia, CA, USA) for RNA
and in K2 EDTA Vacutainer tubes (BD, Franklin Lakes, NJ,
USA) for buffy coat and complete blood count (CBC)
measurements. Cellular RNA was purified from 2.5 mL
of whole blood in PAXgene tubes according to the
manufacturer’s protocols using the RNeasy Mini Kit
(Qiagen, Chatsworth, CA, USA). Total DNA was isolated
from whole blood or buffy coat from EDTA tubes using
QIAamp DNA Blood Mini Kit (Qiagen) according to the
manufacturer’s protocol.
Epigenetic cell counting of lymphocyte subsets using
DNA methylation analysis
Cell counting of lymphocyte subsets was performed by
Epiontis (Berlin, Germany) using quantitative real-time
PCR (qPCR) based DNA methylation analysis [20,21].
Briefly, bisulphite conversion [22] of genomic DNA resulting in either CpG-variants (if DNA is methylated) or
TpG-variants (if DNA is unmethylated) was performed.
Each qPCR assay is specific for either the demethylated
FOXP3 TSDR (for Tregs) or the demethylated CD3D/G
(for T cells) or the demethylated IL17A (for Th17 cells) or
the B cell specific demethylated gene region (for B cells)
templates, since the demethylated version of these regions
have been shown to be exclusively present in Treg, T cells,
Th17 cells and B cells respectively. The other qPCR assay
is specific for a control region within the GAPDH gene, a
target that is demethylated in all cells. The GAPDH PCR
assay serves as a “load control” as it estimates the number
of “total cells” in a given sample. The percentage of Treg
cells, T cells, Th17 cells and B cells in a sample is calculated as:
Percentage of a particular cell-type = [Copy Equivalents
as determined with the PCR assay targeting the cellspecific DNA target region (e.g. TpGTSDR)]/[Copy
Equivalents as determined with the GAPDH qPCR
assay (TpGGAPDH)] × [100] × [2a)].
Singh et al. Allergy, Asthma & Clinical Immunology 2014, 10:32
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In the equation above, the “Copy Equivalents” as determined by the cell-specific PCR assay corresponds to “Treg
cells”, or “T cells” or “Th17 cells” or “B cells” copies, respectively. The “Copy Equivalents” as determined with the
GAPDH PCR assay corresponds to the “total cell” copies,
respectively. A factor of “100” is used to translate the result into percentage of cells.
a)
Only for Tregs a factor of “2” is applied in the equation to correct for the fact that each cell has two copies
of the (demethylated) GAPDH gene but each Treg has
just one copy of the demethylated FOXP3 gene. As
FOXP3 is X-chromosomally located, each Treg holds
exactly one copy of the demethylated FOXP3 gene.
Tregs from male subjects hold one X chromosome on
which the FOXP3 gene is demethylated. In contrast,
each Treg from a female subject has two X chromosomes (and thus two copies of the FOXP3 gene) but one
X chromosome is inactivated (i.e. fully methylated) and
it exists as a Barr body in the cell.
Microarray gene expression assay
Genome-wide expression profiling, labelling and array
hybridization were performed using Affymetrix Human
Gene 1.0 ST arrays (Affymetrix, Santa Clara, CA, USA).
All microarray data has previously [16] been deposited
into the Gene Expression Omnibus (GSE40240). All
‘CEL’ files were normalized using the Robust Multiarray
Average (RMA).
nCounter Elements
Technical replication of selected genes was performed
using a new digital technology, nCounter Elements
(NanoString, Seattle, USA). nCounter Elements allows
users to combine nCounter Elements General Purpose
Reagents (GPRs) with unlabelled probes that target specific genes of interest (www.nanostring.com/elements/).
100 ng of each RNA sample is added to the TagSet in
hybridization buffer and incubated at 65°C for 16 hours.
The TagSet consists of a reporter tag and capture tag that
hybridize to the user designed gene-specific probe A and
probe B complex. Automated processing per cartridge
on the PrepStation (high sensitivity protocol) occurs for
3 hours. After a 2.5 hour scan per cartridge, counts are
acquired from the GEN2 Digital Analyzer. Details regarding data normalization can be found in the supplementary material.
Statistical and bioinformatics analysis
Linear models were used to test the association between
immune cell frequencies and cell-specific gene expression
profiles. Cell counts and all combinations of cell ratios (T, B,
Treg and Th17) were compared using linear regression
models. All microarray data were analysed using the linear models for microarrays (limma) R-library [23]. The
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Benjamini-Hochberg false discovery rate (FDR) was
used to correct for multiple testing. Partial least squares
(PLS), from the mixOmics R-library [24] was used to
identify the relationship between cell-specific gene lists.
Statistical analyses were performed in the statistical
computing program R version 3.0.1 [25].
To test for the enrichment of gene lists, GeneGo network
analysis was performed using MetaCore from Thomson
Reuters. Network analyses were performed on gene lists
created by ranking genes by the scores which rank the
subnetworks to saturation with the objects from the initial
gene list.
Results
Participant characteristics
The 14 participants were classified into eight isolated
early responders (ERs) and six dual responders (DRs), as
shown in Table 1. The mean drop in FEV1 during the late
phase in DRs (21.3 ± 3.2) was 4 times greater (p < 0.05)
compared to ERs (5.1 ± 1.4). Table 1 also shows that all
participants exhibited an immediate drop in FEV1 of
greater than 20%.
Correlation between immune cell frequency and
cell-specific gene expression
Sum of the T cell and B cell frequencies obtained using
the methylation assays strongly correlate (Spearman r = 0.95)
with the lymphocyte frequency obtained using a hematolyzer
(Additional file 1: Figure S1). T cell, B cell and Th17 cell
counts were significantly positively correlated with the
genes targeted in epigenetic cell counting in both the
microarray (Figure 1; top row) and nanoString (Figure 1;
bottom row) platforms. Treg cell counts were not correlated with FOXP3 gene expression measured using microarrays but was significantly correlated using nanoString,
suggesting greater sensitivity of the platform (Figure 1, red
points).
Th17 to Treg ratio discriminates early from dual
responders after challenge
Allergen inhalation did not significantly change T cell, B
cell, Treg cell and Th17 cell counts in either ERs or DRs.
In addition, comparing the change in cell counts in ERs
with the change in cell counts in DRs (ΔER vs. ΔDR) no
significant cell-types were identified (Table 2A). Next, the
ratios between different cell-types were analyzed (Table 2B).
Table 2B shows that the Th17/Treg ratio significantly
(p = 0.03) increased in DRs compared to ERs, from pre
to post challenge. Figure 2 shows that the Th17/Treg
ratio did not change from pre to post challenge in ERs
(net change = 0.006 ± 0.09), whereas the Th17/Treg ratio
increased in DRs (net change = 0.28 ± 0.03).
Singh et al. Allergy, Asthma & Clinical Immunology 2014, 10:32
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Table 1 Participant demographics
Participant
ID
Age (year)
Sex (M:F)
Allergen
Pre [PC20]
(mg/mL)
Post [PC20]
(mg/mL)
Allergen-induced
shiftb
% fall in FEV1
Early
Late
ER
1
28
F
Cat Pelt
12.8
ND
ND
20.3
4.8
2
34
F
Cat Pelt
2.8
6.1
2.3
21
1.5
3
27
M
Cat Pelt
4.5
1.8
0.39
34.4
0
4
42
F
Cat Hair
5.3
8.6
1.6
42.1
11.1
5
29
F
Cat Pelt
0.4
ND
ND
44.3
0
6
31
M
Cat Pelt
11.8
16
1.4
24.2
7.5
7
28
F
Cat Hair
9.4
16
1.7
27.1
7.1
8
42
M
Cat
0.1
ND
ND
23
9
a
a
1.5 ± 0.3
29.6 ± 3.2
5.1 ± 1.4
Mean ± SE
32.6 ± 2.2
3:5
2.8
7.5
DR
9
23
F
Cat Hair
0.3
0.2
0.60
38.9
31.8
10
26
F
Cat Hair
5.1
1.5
0.30
31.4
14.9
11
49
F
Cat Hair
3.6
1.0
0.27
25.3
12.6
12
26
M
Cat Hair
0.9
1.0
1.1
31.5
15.6
13
27
F
Cat Pelt
0.6
0.1
0.19
48.3
25.8
14
52
F
Cat
ND
ND
ND
33
27
a
a
0.5 ± 0.1
34.7 ± 3.2
21.3 ± 3.2c
Mean ± SE
33.8 ± 3.3
1:5
1.3
0.5
ND - Not determined; all participants were challenged with cat allergen.
a
geometric mean (PC20 values are measured on a log scale).
b
[PC20]post/[PC20]pre.
c
p < 0.05 versus ER group.
Figure 1 Scatter plots of immune cells quantified using DNA methylation analysis with the corresponding cell-specific gene expressions
profiles. x-axis: relative cell-type frequencies of T, B, Treg and Th17 cells in whole blood; y-axis: a) top row: gene (CD3D, CD3G, CD79A, CD79A,
FOXP3, and IL17A) expression intensities measured using microarrays and b) bottom row: gene expression counts measured using nCounter
Elements from nanoString.
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Table 2 Comparing immune cell-frequencies and cell/cell ratios between early and dual responders after allergen
challenge
A. Comparing immune cell-frequencies between ERs and DRs after allergen challenge
Fold-change (post-pre) in early responders
Fold-change (post-pre) in dual responders
ΔER vs. ΔDR
Mean ± SEM
Mean ± SEM
p-value
T cell
−2.20 ± 1.40
−4.26 ± 2.39
0.45
B cell
−0.20 ± 0.17
−0.11 ± 0.26
0.77
Th17 cell
−0.22 ± 0.21
0.14 ± 0.16
0.22
Treg cell
−0.12 ± 0.06
−0.42 ± 0.17
0.10
Fold-change (post-pre) in early responders
Fold-change (post-pre) in dual responders
ΔER vs. ΔDR
Mean ± SEM
Mean ± SEM
p-value
B. Comparing immune cell/cell ratios between ERs and DRs after allergen challenge
T/B
−0.59 ± 0.70
−0.74 ± 0.43
0.87
Th17/T
0.002 ± 0.006
0.009 ± 0.002
0.31
Treg/T
−0.0008 ± 0.005
−0.006 ± 0.002
0.38
B/Th17
−0.009 ± 0.10
−0.37 ± 0.21
0.13
B/Treg
−0.002 ± 0.22
0.42 ± 0.18
0.18
Th17/Treg
0.006 ± 0.09
0.28 ± 0.03
0.03
Genes associated with Th17 and Treg cells
A multiple linear regression model (limma) was used to
identify genes whose expression levels correlated with
the frequencies of specific cell-types independent of
changes in the frequencies of other cell-types (gene expression ~ Th17 + Treg + B-cells + other T-cells, where
other T-cells = overall T-cells minus Th17 and Treg).
10 (99) genes were positively correlated with Th17 (Treg)
cells at an FDR of 10%, with no overlapping genes
between the two lists. Th17 genes included KIR2DS2,
TAGLN, C14orf37, KRTAP13-3, SAP30, KIR2DS4, LAIR2,
FLJ30679, RORC and KIR2DL2. The 99 Treg genes were
enriched (FDR = 5%) for 27 pathways including many
relevant regulatory pathways such as IL-2 regulation of
translation, Regulation of telomere length and cellular
immortalization, Regulation of T cell function by CTLA-4
(Additional file 1: Figure S2). Partial Least Squares (PLS)
was used to determine the correlation between the set of
Figure 2 Change in the Th17/Treg ratio in early and dual responders from pre to post challenge. Th17/Treg ratio in ERs (left panel) and
DRs (middle panel) at pre and post-challenge. The change in the Th17/Treg ratio (post-pre) in ERs and DRs (right panel). Solid black points
depict the median value of the data for each boxplot.
Singh et al. Allergy, Asthma & Clinical Immunology 2014, 10:32
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10 Th17 genes and the set of 99 Treg genes. Figure 3 depicts the results of PLS using a correlation circle (see
Gonzalez et al. [26] for complete details on graphical
outputs of PLS). Vectors drawn from the origin to each
of the points (genes) allows one to determine the relationship between genes: 1) if the angle between two vectors is
less than 90°, there exists a positive correlation between
the two genes, 2) if the angle between two vectors is
greater than 90°, there exists a negative correlation between the two genes, and 3) if the angle between two vectors is equal to 90°, the correlation between the two genes
is zero. Figure 3 shows that the Th17 genes were inversely
correlated with Treg genes (angle greater than 90°).
Genes significantly correlated with the Th17/Treg ratio
To investigate the relationship of Th17/Treg ratio and
gene expression profiles, we identified correlated genes
in the entire sample set. We identified 13 genes significantly
correlated with Th17/Treg ratio using limma (FDR = 5%,
Table 3). Interestingly, 7 genes (KIR3DL1, LAIR2, KIR2DS2,
KIR2DL2, CD226, KIR2DS4, KIR2DS1) belong to the
leukocyte receptor complex (LRC) located on chromosome 19q13.4, and were shown to be positively correlated except CD226. However, of the four genes profiled
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Table 3 Genes significantly correlated to Th17/Treg ratio
in Pearson tests (FDR <0.05)
Gene
r
p value
FDR
TAGLN
0.78
7.59E-07
0.02
C14orf37
0.77
3.16E-06
0.02
KIR3DL1*
0.75
3.82E-06
0.02
LAIR2*
0.75
4.80E-06
0.02
CDCP1
0.74
5.77E-06
0.02
KIR2DS2*
0.74
6.19E-06
0.02
SAP30
0.74
7.09E-06
0.02
KIR2DL2*
0.73
8.51E-06
0.02
CD226*
−0.73
9.83E-06
0.02
ZNF286B
−0.73
9.27E-06
0.02
KRTAP13-3
0.72
1.57E-05
0.03
KIR2DS4*
0.71
2.38E-05
0.043
KIR2DS1*
0.70
2.86E-05
0.048
*genes belonging to leukocyte receptor complex (LRC).
using nanoString, only CD226 and KIR2DS4 successfully replicated (Figure 4). The top-listed transcriptional
network in GeneGo network analysis for the 13 significant
genes included regulatory functions in immune responses
(Additional file 1: Table S1).
Significantly different genes between ERs and DRs in
Th17 or Treg
Figure 3 Correlation circle depicting the strength of correlation
between Treg genes (red) and Th17 genes (blue) with their
respective latent variables (Comp 1 and Comp 2). The Treg
genes (red) show a strong negative correlation with the Th17 genes
(blue). Vectors drawn from the origin to each of the points (genes)
allows one to determine the relationship between genes: 1) if the
angle between two vectors is less than 90°, there exists a positive
correlation between the two genes, 2) if the angle between two
vectors is greater than 90°, there exists a negative correlation
between the two genes, and 3) if the angle between two vectors is
equal to 90°, the correlation between the two genes is zero.
In a secondary analysis, we also analysed gene-cell correlations that significantly differed between early and dual responders, irrespective of allergen exposure, using limma.
In GeneGo network analysis, genes differentially associated with Th17 (165 genes differentially associated with
Th17 between ERs and DRs, p < 0.01) were enriched for
immunological processes including immunoglobulin mediated immune response and adaptive immune response.
Genes differentially associated with Treg between ER and
DR (554 genes, p < 0.01) were enriched for immune processes. Although the genes differentially associated with
Th17 cells between ERs and DRs did not achieve a stringent threshold of FDR, the top three genes, S100B, MILR1
and CHI3L1 (p-value < 0.001, FDR = 0.79, Additional file
1: Figure S3), have previously been reported to be involved
in allergy or asthma [27-29]. Additional file 1: Figure S3
shows that all three genes were differentially correlated
with Th17 cell counts with respect to the response class
using both the microarray and nanoString platforms.
Discussion
Although Th17 and Treg cells arise from a common
precursor cell [30] they have opposing inflammatory
roles which has been demonstrated in the context of
autoimmune disease [31], infection [32], and recently
allergic airway inflammation [1]. In the present study
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Figure 4 Scatter plots of genes significantly correlated with the Th17/Treg ratio. x-axis: Th17/Treg ratio; y-axis: a) top row: gene (CD226,
KIR2DS4, KIR3DL1, and LAIR2) expression intensities measured using microarrays and b) bottom row: gene expression counts measured using
nCounter Elements from nanoString.
we demonstrate a potential Th17/Treg homeostatic imbalance using peripheral blood of isolated early and dual
asthmatic responders (ERs and DRs) undergoing allergen
inhalation challenge.
DNA methylation analysis used to enumerate various
immune cells revealed good correlation with the cellspecific gene expression profiles as measured using microarrays. Technical replication using nCounter Elements
from nanoString, a more sensitive platform indicated that
FOXP3 expression was indeed correlated with Treg cell
counts. As a marker for human Tregs, however, FOXP3
expression is of doubtful value, due to its transient expression in activated non-regulatory effector T cells [21]. In
addition, other cell-surface markers such as CD127 or
CD45RA have been used to isolate FOXP3+ Treg cell populations with high efficiency [33,34]. Epigenetic enumeration of Treg cells in the present study has been shown to
positively correlate with CD4+CD25+CD127lo, and CD4+
CD25+CD127loFOXP3+ [12] and thus are truly representative of suppressive Tregs.
The percentage of Treg cells did not significantly change
in either ERs (−0.12 ± 0.06; p = 0.11) or DRs (−0.42 ± 0.17;
p = 0.054), two hours post-challenge. Previous studies have
also not shown significant changes in Treg cells in peripheral blood in DRs undergoing allergen inhalation challenge [35,36]. This may be due to many factors such as
the time of the post-challenge blood draw, the cell-surface
markers used to isolate the Treg cells as well as the small
sample sizes (n = 6-11) used in these studies. Similarly, the
percentage of Th17 cells also did not significantly change
in ERs (−0.22 ± 0.21; p = 0.30) or DRs (0.14 ± 0.16; p = 0.44),
after allergen challenge. Th17 cells have been shown to be
increased 7 and 24 hours post-challenge in both ERs and
DRs and the increase in DRs was greater than in ERs
24 hours post challenge [5]. Th17 cells as well as the concentrations of IL-17 and IL-22, have also been shown to
be increased with the severity of allergic asthma [37].
Genes significantly positively correlated with Th17 cells
included RORC, the transcription factor involved in Th17
differentiation, whereas genes significantly positively correlated with Treg genes was enriched for regulatory functions. Furthermore, Th17 and Treg cell associated genes
were inversely correlated with each other, further implicating the phenotypic roles of these cell-types in allergic
asthma.
Although neither cell-type significantly changed pre to
post challenge, the change in the Th17/Treg ratio from
pre to post challenge significantly (p = 0.03) differed between ERs and DRs. The Th17/Treg ratio increased in
DRs whereas little change occurs in ERs after challenge.
The increase in the Th17/Treg ratio in DRs is driven by
an increase in the number of Th17 cells (0.14 ± 0.16)
and a decrease in the number of Treg cells (−0.42 ±
0.17) due to allergen exposure. A possible mechanism of
Th17/Treg imbalance was suggested by the genes that
were correlated with Th17/Treg ratio. LRC on chromosome 19q13.4 encodes immunoglobulin super family receptors including killer immunoglobulin like receptors
(KIRs) expressed on hematopoietic cells. Almost all LRC
significant genes were positively correlated to Th17/Treg,
whilst CD226 is the only LRC gene negatively correlated.
A previous study on differential expression of LRC genes
Singh et al. Allergy, Asthma & Clinical Immunology 2014, 10:32
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revealed that KIRs and inhibitory receptor ILT2/LIR1
were expressed in activated T cells and that KIR levels in
T cells are associated with resistance to activation-induced
cell death [38]. These may suggest a new hypothesis that
LRC gene expression patterns might be related to Th17/
Treg ratio and involved in immune responses to inhaled
allergen in asthmatics.
The statistical interaction analyses suggested differences
in gene expression profiles in Th17 or Tregs between ERs
and DRs. Interestingly, top-listed differentially expressed
Th17-associated genes S100B, MILR1 and CHI3L1 have
been reported to play roles in allergy and asthma. S100B+
lymphocytes in blood have been reported to consist of
two subtypes; a cytotoxic T cell and a NK subtype [27]. In
connection with the significant correlations between Th17
and KIR family, Th17 measured by epigenetic cell counting for IL17A might be related to other types of immune
cells. This is supported by reports showing that IL-17
genes are expressed in non-CD4+ T cells such as γδ T
cells, NK cells and Type 3 innate lymphoid cells, suggesting that innate immunity might be responsible for initiating this type of inflammation commonly associated with
Th17 immunity [39,40]. Further studies are needed to
clarify the disparity between true Th17 and IL17Ademethylated cells. MILR1 is the gene for allergin-1 protein, which was recently identified to play an inhibitory
role in mast cell functions [28]. Polymorphisms in CHI3L1
as well as the concentration of its corresponding protein
YKL-40 in serum has been associated with asthma and
pulmonary function [29]. Our findings suggest that Th17
cell gene expression profiles are divergent between asthmatic responses and that these profiles might be related to
immune mechanisms.
A limitation of this study is its small sample size,
which reduces the statistical power in identifying true
positives. Therefore we deemed a technical validation
using a highly sensitive platform appropriate for this
study. Independent replication will be important as part
of future studies with larger sample sizes. Another limitation of the present study is that only a limited number
of cell-types were studied using DNA methylation analysis, whereas quantification of a wide array of cell-types
such as Th1, Th2, and Th9 cells would provide deeper
biological insights into the mechanisms of allergic asthmatic responses. DNA methylation based qPCR assays for
these cell-types will allow for tissue samples to remain unperturbed, and additional sources of variability, such as
those observed in fluorescence activated cell sorting to be
avoided.
The careful phenotyping of our participants, together
with innovative epigenetic- and gene expression-based
methodologies, have nevertheless revealed interesting directions for further investigations using large sample
sizes and different allergens.
Page 8 of 9
Availability of supporting data
Supplementary Tables and Figures are shown in “Additional
Documentation”.
Additional file
Additional file 1: Figure S1. Epigenetic vs. hematolyzer correlation of
relative lymphocyte counts. Figure S2. Pathway analysis of 99 genes
positively significantly associated with Treg cells. Only pathway with
FDR<5% are shown. Figure S3. Correlation between genes and Th17
cells that differ between early and dual responders. Table S1. Top-listed
transcription regulation network in GeneGo analysis for the genes significantly
correlated to Th17/Treg ratio (FDR < 0.05).
Abbreviations
Th17: T helper 17; Treg: Regulatory T; FOXP3: Forkhead box protein 3;
TSDR: Treg specific demethylation region; ER: Isolated early responder;
DR: Dual responder; Limma: Linear models for microarrays; PLS: Partial least
squares; LRC: Leukocyte receptor complex.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
AS and MY contributed equally to this work. GMG, PMO, CC, JMF, LPB, SJT
participated in research design and provision of samples. MY, JR, SJT
participated in the sample processing and following experiments. SO and
UH performed the epigenetic cell counting assay. AS, MY, JYC, SJT
conducted data analyses. AS, MY, CC, SJT participated in the writing of the
paper. All authors read and approved the final manuscript.
Acknowledgements
We thank the research participants for taking part in these studies, as well as
Johane Lepage, Philippe Prince, Joanne Milot, Mylène Bertrand, Richard
Watson, George Obminski, Heather Campbell, Abbey Torek, Tara Strinich and
Karen Howie for their expertise and assistance with participant recruitment,
allergen challenge and sample collection, as part of the AllerGen NCE Clinical
Investigator Collaborative. We also thank Peter Paré for technical advice. This
research was supported by funding from AllerGen NCE Inc. (Allergy, Genes
and Environment Network) and the Canadian Institutes of Health Research.
We are grateful for additional funding support from the Peter Wall Institute
for Advanced Studies. AS is the recipient of the CIHR Doctoral Award – Frederick
Banting and Charles Best Canada Graduate Scholarship. MY was supported in
part by the fellowship grants of a Canadian Institutes of Health Research (CIHR)
Integrated and Mentored Pulmonary and Cardiovascular Training Program
(IMPACT), the Sumitomo Life Social Welfare Services Foundation and the
Mochida Memorial Foundation for Medical and Pharmaceutical Research.
Author details
1
James Hogg Research Centre for Heart Lung Innovation, St. Paul’s Hospital,
University of British Columbia, Vancouver, BC, Canada. 2Institute for HEART +
LUNG Health, Vancouver, BC, Canada. 3Prevention of Organ Failure (PROOF)
Centre of Excellence, Vancouver, BC, Canada. 4Vancouver Coastal Health
Research Institute, Vancouver General Hospital, Vancouver, BC, Canada.
5
Department of Medicine, Division of Respiratory Medicine, UBC, Vancouver,
BC, Canada. 6Department of Medicine, McMaster University, Hamilton, ON,
Canada. 7Epiontis GmbH, Berlin, Germany. 8Quebec Heart and Lung Institute,
Laval University, Québec City, QC, Canada.
Received: 19 April 2014 Accepted: 2 June 2014
Published: 24 June 2014
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Cite this article as: Singh et al.: Th17/Treg ratio derived using DNA
methylation analysis is associated with the late phase asthmatic
response. Allergy, Asthma & Clinical Immunology 2014 10:32.